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Accelerating Streamline Tracking in Groundwater Flow Modeling on GPUs.

Xiaohui Ji1, Mulan Luo1, Xu-Sheng Wang2

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This summary is machine-generated.

This study introduces a parallel graphics processing unit (GPU) method to accelerate streamline simulations in groundwater flow modeling, significantly reducing computation time for large datasets.

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Area of Science:

  • Hydrogeology
  • Computational Science
  • Parallel Computing

Background:

  • Streamline simulation in groundwater flow modeling is computationally intensive.
  • Analyzing numerous streamlines for accurate flow path determination is time-consuming.
  • Existing methods lack efficiency for large-scale simulations.

Purpose of the Study:

  • To develop and implement a parallelized graphics processing unit (GPU) method for accelerating streamline simulations.
  • To enhance the efficiency of the semi-analytical particle tracking algorithm.
  • To enable faster identification of three-dimensional (3D) groundwater flow systems.

Main Methods:

  • Developed a parallelization method using Compute Unified Device Architecture (CUDA) on GPUs.
  • Implemented individual threads for forward and backward streamline tracking.
  • Utilized multi-GPUs to process millions of particles for accelerated streamline tracking.

Main Results:

  • Achieved significant speedup in streamline simulation using multi-GPUs.
  • Demonstrated speedups exceeding 1000x with 8 NVIDIA GPUs for over 5 million streamlines.
  • Successfully applied the method to identify 3D flow systems in a Tóthian basin model.

Conclusions:

  • The parallel GPU method dramatically accelerates groundwater flow streamline simulations.
  • This approach offers a viable solution for analyzing large numbers of streamlines efficiently.
  • Enables more rapid and detailed characterization of complex 3D groundwater flow systems.